Our Mission

The mission of the journal Homeostasis is to build a testable science of homeostasis that improves patient care and population health. By bridging experimental biology with AI, machine learning, computational sciences, and clinical translation, the journal Homeostasis aims to be a leading platform for shaping the future of predictive and precision medicine.

Aims

Homeostasis refers to the dynamic process through which internal variables are actively regulated to remain within life-sustaining limits, even in the face of external perturbations. The journal Homeostasis publishes mechanistic and translational studies that explain, measure, and modulate control processes across molecular, cellular, organ, and whole-organism levels. The aim is to develop diagnostic, monitoring, and therapeutic tools that make use of homeostasis predictions. We place special emphasis on how artificial intelligence (AI), machine learning and computational modeling can transform these aims by enabling deep learning, predictive diagnostics, and precision interventions.

Scope

We encourage work that integrates AI-driven approaches to map these dynamic trade-offs and predict maladaptation before clinical manifestation. We publish data showing that disease often reflects failure or maladaptation of dynamic homeostatic controls. We consider studies that quantify stability, disturbance, and control performance in living systems, including
• Feedforward control, adaptive gain, and set-point resetting that refine performance across circadian phase, stress, infection, growth, and aging
• Thermoregulation and energy balance
• Neuroendocrine and autonomic control
• Immunometabolic and inflammatory regulation
• Oxidative and nitrosative stress and proteostasis
• Microbiome–host interactions
• Sleep and circadian timing
• Development, aging, and homeodynamics
• Comparative and environmental physiology
• Behavior that anticipates or counters disturbance
• Fluid, electrolyte, and acid–base balance
• Osmoregulation and volume regulation
• AI-enhanced modeling of physiological resilience and adaptive capacity
• Digital biomarkers and wearable sensor integration for continuous homeostasis monitoring

Methods and approaches

• Interventional studies and clinical trials with rigorous design and statistics
• Hybrid experimental–computational pipelines that combine laboratory data with AI-driven inference to accelerate mechanistic discovery
• Human, animal, and in-vitro models, including organoids, organ-on-chip, and closed-loop devices
• Systems identification, control theory, and dynamical modeling
• Causal inference and analysis of physiological time series
• Advanced AI and machine learning for multimodal physiological data integration, digital twin models, and predictive simulations of system stability
• Single-cell and spatial omics
• Proteomics, metabolomics, and lipidomics
• Imaging and biosensors

Disease and application domains

• Metabolic and endocrine disorders
• Critical illness and sepsis
• Heart and kidney failure
• Hepatic and pulmonary disease
• Neurological and psychiatric conditions
• Exercise, altitude, and environmental physiology
• Pharmacological and toxicological perturbations
• AI-enabled precision medicine approaches for diagnosis, prognosis, and therapy optimization in these conditions

Article types and policies

We publish Research research, Review/Mini Review, Systematic Review, Short Communication, Perspective and Editorial.
Manuscripts must follow transparent protocols and sound statistics. Data, codes, and models should be shared when possible. Authors should align with CONSORT, STROBE, ARRIVE, and PRISMA where relevant. We prioritize work that integrates across scales, quantifies control performance, or demonstrates actionable interventions that restore or reconfigure stability. Submissions that leverage AI for integrative analyses, reproducible workflows, or explainable models are strongly encouraged.

Audience

The journal serves physiologists, clinicians, bioengineers, computational scientists, and public-health researchers.